A comprehensive collection of 70 NumPy exercises with detailed problem statements, code snippets, and expected outputs that cover array creation, manipulation, statistical analysis, handling missing data, encoding, sorting, and advanced mathematical operations for Python developers.
This article explains why machine learning relies on big‑data statistical learning, illustrating human learning through induction and deduction, presenting case studies that highlight the limits of anecdotal reasoning, and introducing the law of large numbers and probabilistic trust as foundations for reliable AI models.
At JSM 2019 in Denver, Didi’s AI Labs returned with four accepted papers on equilibrium metrics, EV ride‑sharing, supply‑balance and user lifetime value, delivered a keynote on AI for marketplaces, hosted a technical salon, and used the conference as a recruiting showcase.
This article examines the core challenges of query optimization in relational databases—accurate statistics, massive plan spaces, and efficient plan management—and explains how OceanBase addresses them through logical/physical row concepts, real‑time statistics, distributed two‑stage planning, adaptive caching, and plan evolution mechanisms.
This comprehensive guide explores a wide range of anomaly detection techniques—including time‑series methods, statistical models, distance‑based approaches, tree‑based isolation forests, graph algorithms, behavior‑sequence Markov models, and supervised machine‑learning models—detailing their principles, formulas, and practical scenarios for detecting outliers in advertising, fraud, and system monitoring.
This article explains the various roles of data scientists, compares data science with machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics, and clarifies the distinctions and overlaps among these fields.
This article introduces the concept of anomaly detection, outlines common application scenarios such as ELT pipelines, feature engineering, A/B testing, and fraud detection, and reviews various detection methods—including statistical models, machine learning, rule‑based logic, and density‑based techniques—while discussing practical implementation considerations.
This article demonstrates how to use Python to simulate coin tosses, construct the likelihood function for a Bernoulli distribution, and apply maximum likelihood estimation to recover the underlying head probability, illustrating the method with code, equations, and visualizations.
This guide explains how to set up HAProxy, covering core concepts such as ACLs, backends, frontends, optional statistics, a complete configuration example, and step‑by‑step deployment on Linux, enabling you to quickly build a reliable load‑balancing proxy.
The article explains what a big data development engineer does, the tools and skills required such as Hadoop, Hive, Spark and Kafka, how they process massive logs to compute metrics like PV and UV, and compares this role with conventional business system development.
This article explains the concept of maximum likelihood estimation (MLE) and demonstrates how to estimate the probability of heads in a coin‑toss experiment using Python simulation, sympy for the likelihood function, and visualisation of the resulting discrete probability distribution.
This tutorial shows how to load Apple stock data from a CSV file using NumPy, then compute basic statistics such as mean, weighted average, min/max, variance, daily returns, volatility, and weekday‑based price averages, illustrating essential data‑analysis functions for finance.
This article uses a dialogue‑driven story to explain how PostgreSQL gathers statistics, computes selection rates, evaluates various physical scan and join paths, and combines CPU, I/O, and communication costs—including startup versus total cost—to choose the most efficient execution plan.
This tutorial walks through loading Apple stock CSV data with NumPy, computing basic statistics like mean, median, variance, weighted average, daily returns, volatility, and handling dates, while demonstrating essential NumPy functions and code snippets for practical financial data analysis.
The article explains the Poisson distribution, its probability mass function, the three conditions required for its use, provides real‑world examples such as bus arrivals and radioactive decay, and illustrates its cumulative curve and why many everyday phenomena fit this model.
This article explains the concepts of entropy, joint entropy, conditional entropy, relative entropy (KL divergence) and cross‑entropy, illustrating their definitions, mathematical formulas, intuitive interpretations, and relationships through simple probability examples and visual diagrams.
This article explains the internal workings of the SHOW ENGINE ROCKSDB STATUS command in MyRocks, detailing how STATISTICS and BG_THREADS data are collected, the underlying RocksDB APIs, code structures, and how to extend these mechanisms for performance monitoring and debugging.
This article explains the statistical foundations of A/B experiments—including population, sample, sampling error, confidence intervals, hypothesis testing, type I/II errors, statistical significance, and power—so engineers can design reliable tests and interpret results with confidence.
This article compiles over twenty‑seven curated cheat sheets covering machine learning algorithms, Python data‑science libraries, and essential mathematics such as probability, linear algebra, statistics, and calculus, providing concise reference images and source links for quick study.
A Python simulation of 1,000 gamblers playing daily dice bets shows an 83.3% bankruptcy rate, with half going broke within a year, revealing why even a 50% win chance leads most players to ruin despite occasional high earners.
This article explains what statistics entails, outlines its core tools and methods—including data collection, processing, analysis, and visualization—describes essential software and programming skills, and explores career routes for statisticians and data analysts in both technical and business roles.
This article explains the fundamentals of Oracle SQL execution plans, how to read and interpret them, the role of statistics in plan selection, methods for gathering statistics, various techniques for obtaining execution plans, and practical tips for identifying inefficient SQL statements.
This article explains what an Oracle SQL execution plan is, how statistics guide the optimizer, various ways to retrieve plans, the classification of plan types, and practical tips for spotting and fixing inefficient plans using real‑world examples and code snippets.
This article compiles a wide range of interview questions sourced from Glassdoor covering general topics, machine learning, statistics, programming, big‑data frameworks, SQL, and brain‑teasers, providing candidates with English translations and insights into the types of problems asked by companies such as Apple, Google, Microsoft, Uber, and many others.
This article explains the essential statistical concepts—populations, samples, sampling error, confidence intervals, the Central Limit Theorem, and normal distribution—that underpin A/B testing, showing how they enable reliable hypothesis evaluation, accurate impact prediction, and data‑driven decision making for product experiments.
This article walks through a real‑world Oracle SQL tuning case, showing how to dissect a 12‑hour execution plan, fix cardinality and statistics issues, eliminate costly Filter operations, rewrite queries for efficient HASH JOINs, and leverage tools like 10053 and SQLT to dramatically improve performance.
This article surveys the most essential Python packages for data science, covering core scientific libraries, visualization tools, machine‑learning frameworks, natural‑language‑processing kits, and data‑mining utilities, with brief descriptions and links to each project.
The article reviews the surge of AI interest sparked by AlphaGo and Master, explains why strong mathematics—especially linear algebra, probability, statistics, calculus, and optimization—is crucial for AI practitioners, and provides curated free online courses, textbooks, and resources to help beginners master these subjects.
A colleague handed over a seemingly simple SQL Server query that ran in five seconds for early dates but never returned for later dates, and the article walks through the diagnosis, execution‑plan analysis, statistics update, and final fix that restored performance.
This guide explains when and why Oracle statistics are needed, how to control automatic and manual collection, prevent out‑of‑range errors, boost efficiency with parallel and incremental methods, and handle special cases like volatile or temporary tables for optimal query performance.
This guide explains the role of optimizer statistics in Oracle, when to gather them, how to use automatic and manual collection jobs, tune ESTIMATE_PERCENT and METHOD_OPT, manage histograms, handle bind peeking, apply dynamic sampling, and safely test changes with pending statistics.
The article explains how Oracle’s default DBMS_STATS.AUTO_SAMPLE_SIZE can produce wildly inaccurate cardinality estimates on highly skewed columns, examines the NewDensity and OldDensity algorithms, demonstrates the problem with real‑world examples, and offers practical solutions such as forcing estimate_percent or disabling NewDensity.
This article provides a comprehensive guide to Baidu's mobile SDKs—including Statistics, Map (location), and Voice—detailing their features, platform-specific integration steps for Android and iOS, and sample code snippets for developers to implement analytics, precise positioning, and speech recognition in their apps.
This guide explains how to verify and configure Oracle's automatic optimizer statistics collection, customize collection windows, tailor statistics for large and partitioned tables, detect stale statistics, and decide when to gather histograms, providing practical SQL and PL/SQL examples.
This article explains the classic Oracle batch‑processing issue caused by inaccurate statistics on tables with sharply changing row counts, reviews traditional mitigation methods and their drawbacks, and introduces Oracle 12c's online statistics gathering for bulk‑load as an automated solution while noting its current limitations.
This article walks through practical steps for identifying performance bottlenecks in PostgreSQL, selecting appropriate columns and index types, interpreting system statistics, and evaluating cost estimates with real‑world examples to dramatically reduce query latency.
This article explores the major enhancements in Oracle 12c's optimizer, including session‑level statistics for global temporary tables, new DBMS_STATS reporting procedures, adaptive features, advanced join elimination methods, incremental and concurrent statistics collection, and automatic column‑group detection, providing practical guidance for DBAs and developers.
This technical guide explores Oracle 12c's optimizer enhancements, covering adaptive query optimization, adaptive plans and join methods, hybrid hash distribution, dynamic and adaptive statistics, automatic re‑optimization, statistics feedback, new histogram types, and online statistics collection, with practical examples and visual illustrations.
The article explains why small data are essential for avoiding common big‑data mining traps, illustrates pitfalls through real‑world examples, and offers practical methods—incremental improvement, analogical reasoning, and simple modeling—to harness weak signals for more reliable decision‑making.
This article explains how to use Redis bit operations and bitmap keys to record daily user logins and efficiently compute statistics such as today’s active users, users active over the past three days, and users active within a week.
This article introduces exploratory factor analysis as a powerful dimensionality‑reduction method, explains its historical origins, describes its relationship to confirmatory factor analysis, and demonstrates its practical use in consumer‑value research by extracting four interpretable factors.
A slow Oracle OLTP query that consumed 56% of DB resources and averaged 28 seconds per execution was dramatically accelerated to millisecond response time by forcing the proper index, refreshing stale table statistics, and ultimately creating a cn + c_date composite index.
This article outlines practical strategies for Oracle DBAs to design, automate, and fine‑tune statistics‑gathering processes in large, high‑concurrency environments, covering automatic options, handling skewed data, partitioned tables, bind variables, and ongoing optimization cycles.
This article walks through a real Oracle OLTP query that took 28 seconds per execution, explains how execution‑plan analysis, index hints, and correcting stale statistics reduced the runtime to sub‑millisecond levels, and shows the final combined index that eliminated the performance issue.
This article explains how Oracle's Cost‑Based Optimizer estimates the selectivity of range predicates without histograms, demonstrates calculations with sample data, shows PL/SQL code for converting string values to internal numbers, and discusses pitfalls such as identical internal values causing cardinality errors, offering solutions like gathering histograms or using DATE columns.
The report surveyed 1 million Chinese programmers in Q4 2014, revealing that most are 20‑40, male-dominated, concentrated in tech hubs, primarily use PCs with Windows 7, Chrome, and languages C++/C, while mobile usage favors Android and iOS.
In this interview, veteran Oracle DBA Shi Yuedong shares his career journey, personal philosophy on opportunity, a hands‑on performance‑diagnosis case at Lenovo, experiments revealing Oracle 12.1's default statistics sampling rate, and thoughtful perspectives on big data versus data‑warehouse evolution.
